Publications
Publication Information
| Title | Charged Particle Reconstruction in CLAS12 using Machine Learning |
| Authors | Gagik Gavalian, Polykarpos Thomadakis, Kevin Garner, Nikos Chrisochoides |
| JLAB number | JLAB-PHY-23-3757 |
| LANL number | (None) |
| Other number | DOE/OR/23177-5728 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Computer Physics Communication Volume 287 Issue 1 Page(s) 108694 Refereed | |
| Publication Abstract: | In this work, we present studies of track parameter reconstruction from raw information in CLAS12 detector's Drift Chambers, using Machine Learning (ML). We study the resolution of tracks reconstructed with different types of ML models/algorithms, including Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Gradient Boosting Trees (GBT) using simulated data. The resulting ML model is capable of reconstructing track parameters (particle momentum, and polar and azimuthal angles) with accuracy similar to Hit Based (HB) tracking code, but $150$ times faster. Moreover, physics reactions can be identified using the particles reconstructed by the neural network in real-time (with a rate of about $34~kHz$) during experimental data collection. The developed model can be used in numerous applications, such as triggering specific physics reactions in real-time, detector performance monitoring, and real-time detector calibration. |
| Experiment Numbers: | E12-06-112 |
| Group: | Hall B |
| Document: | |
| DOI: | https://doi.org/10.1016/j.cpc.2023.108694 |
| Accepted Manuscript: | main45.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |